feat: GenAI SDK client(multimodal) - Support Assess Batch Prediction Validity for the multimodal datasets.

tests/unit/vertexai/genai/replays/test_assess_multimodal_dataset.py

@@ -111,6 +111,28 @@ def test_assess_batch_prediction_resources(client):
     assert isinstance(response, types.BatchPredictionResourceUsageAssessmentResult)
 
 
+def test_assess_batch_prediction_validity(client):
+    response = client.datasets.assess_batch_prediction_validity(
+        dataset_name=DATASET,
+        model_name="gemini-2.5-flash-001",
+        template_config=types.GeminiTemplateConfig(
+            gemini_example=types.GeminiExample(
+                contents=[
+                    {
+                        "role": "user",
+                        "parts": [{"text": "What is the capital of {name}?"}],
+                    },
+                    {
+                        "role": "model",
+                        "parts": [{"text": "{capital}"}],
+                    },
+                ],
+            ),
+        ),
+    )
+    assert isinstance(response, types.BatchPredictionValidationAssessmentResult)
+
+
 pytestmark = pytest_helper.setup(
     file=__file__,
     globals_for_file=globals(),
@@ -206,3 +228,26 @@ async def test_assess_batch_prediction_resources_async(client):
         ),
     )
     assert isinstance(response, types.BatchPredictionResourceUsageAssessmentResult)
+
+
+@pytest.mark.asyncio
+async def test_assess_batch_prediction_validity_async(client):
+    response = await client.aio.datasets.assess_batch_prediction_validity(
+        dataset_name=DATASET,
+        model_name="gemini-2.5-flash-001",
+        template_config=types.GeminiTemplateConfig(
+            gemini_example=types.GeminiExample(
+                contents=[
+                    {
+                        "role": "user",
+                        "parts": [{"text": "What is the capital of {name}?"}],
+                    },
+                    {
+                        "role": "model",
+                        "parts": [{"text": "{capital}"}],
+                    },
+                ],
+            ),
+        ),
+    )
+    assert isinstance(response, types.BatchPredictionValidationAssessmentResult)

vertexai/_genai/datasets.py

@@ -1180,6 +1180,65 @@ def assess_batch_prediction_resources(
             types.BatchPredictionResourceUsageAssessmentResult, result
         )
 
+    def assess_batch_prediction_validity(
+        self,
+        *,
+        dataset_name: str,
+        model_name: str,
+        template_config: Optional[types.GeminiTemplateConfigOrDict] = None,
+        config: Optional[types.AssessDatasetConfigOrDict] = None,
+    ) -> types.BatchPredictionValidationAssessmentResult:
+        """Assess if the assembled dataset is valid in terms of batch prediction
+          for a given model. Raises an error if the dataset is invalid, otherwise
+          returns None.
+
+        Args:
+          dataset_name:
+            Required. The name of the dataset to assess the batch prediction
+            validity for.
+          model_name:
+            Required. The name of the model to assess the batch prediction
+            validity for.
+          template_config:
+              Optional. The template config used to assemble the dataset
+              before assessing the batch prediction validity. If not provided, the
+              template config attached to the dataset will be used. Required
+              if no template config is attached to the dataset.
+          config:
+            Optional. A configuration for assessing the batch prediction validity.
+            If not provided, the default configuration will be used.
+
+        Returns:
+            A types.BatchPredictionValidationAssessmentResult object representing
+            the batch prediction validity assessment result.
+            It contains the following keys:
+              - errors: A list of errors that occurred during the batch prediction
+                validity assessment.
+        """
+        if isinstance(config, dict):
+            config = types.AssessDatasetConfig(**config)
+        elif not config:
+            config = types.AssessDatasetConfig()
+
+        operation = self._assess_multimodal_dataset(
+            name=dataset_name,
+            batch_prediction_validation_assessment_config=types.BatchPredictionValidationAssessmentConfig(
+                model_name=model_name,
+            ),
+            gemini_request_read_config=types.GeminiRequestReadConfig(
+                template_config=template_config,
+            ),
+            config=config,
+        )
+        response = self._wait_for_operation(
+            operation=operation,
+            timeout_seconds=config.timeout,
+        )
+        result = response["batchPredictionValidationAssessmentResult"]
+        return _datasets_utils.create_from_response(
+            types.BatchPredictionValidationAssessmentResult, result
+        )
+
 
 class AsyncDatasets(_api_module.BaseModule):
 
@@ -2127,3 +2186,62 @@ async def assess_batch_prediction_resources(
         return _datasets_utils.create_from_response(
             types.BatchPredictionResourceUsageAssessmentResult, result
         )
+
+    async def assess_batch_prediction_validity(
+        self,
+        *,
+        dataset_name: str,
+        model_name: str,
+        template_config: Optional[types.GeminiTemplateConfigOrDict] = None,
+        config: Optional[types.AssessDatasetConfigOrDict] = None,
+    ) -> types.BatchPredictionValidationAssessmentResult:
+        """Assess if the assembled dataset is valid in terms of batch prediction
+          for a given model. Raises an error if the dataset is invalid, otherwise
+          returns None.
+
+        Args:
+          dataset_name:
+            Required. The name of the dataset to assess the batch prediction
+            validity for.
+          model_name:
+            Required. The name of the model to assess the batch prediction
+            validity for.
+          template_config:
+              Optional. The template config used to assemble the dataset
+              before assessing the batch prediction validity. If not provided, the
+              template config attached to the dataset will be used. Required
+              if no template config is attached to the dataset.
+          config:
+            Optional. A configuration for assessing the batch prediction validity.
+            If not provided, the default configuration will be used.
+
+        Returns:
+            A types.BatchPredictionValidationAssessmentResult object representing
+            the batch prediction validity assessment result.
+            It contains the following keys:
+              - errors: A list of errors that occurred during the batch prediction
+                validity assessment.
+        """
+        if isinstance(config, dict):
+            config = types.AssessDatasetConfig(**config)
+        elif not config:
+            config = types.AssessDatasetConfig()
+
+        operation = await self._assess_multimodal_dataset(
+            name=dataset_name,
+            batch_prediction_validation_assessment_config=types.BatchPredictionValidationAssessmentConfig(
+                model_name=model_name,
+            ),
+            gemini_request_read_config=types.GeminiRequestReadConfig(
+                template_config=template_config,
+            ),
+            config=config,
+        )
+        response = await self._wait_for_operation(
+            operation=operation,
+            timeout_seconds=config.timeout,
+        )
+        result = response["batchPredictionValidationAssessmentResult"]
+        return _datasets_utils.create_from_response(
+            types.BatchPredictionValidationAssessmentResult, result
+        )

vertexai/_genai/types/__init__.py

@@ -148,6 +148,9 @@
 from .common import BatchPredictionValidationAssessmentConfig
 from .common import BatchPredictionValidationAssessmentConfigDict
 from .common import BatchPredictionValidationAssessmentConfigOrDict
+from .common import BatchPredictionValidationAssessmentResult
+from .common import BatchPredictionValidationAssessmentResultDict
+from .common import BatchPredictionValidationAssessmentResultOrDict
 from .common import BigQueryRequestSet
 from .common import BigQueryRequestSetDict
 from .common import BigQueryRequestSetOrDict
@@ -1908,6 +1911,9 @@
     "BatchPredictionResourceUsageAssessmentResult",
     "BatchPredictionResourceUsageAssessmentResultDict",
     "BatchPredictionResourceUsageAssessmentResultOrDict",
+    "BatchPredictionValidationAssessmentResult",
+    "BatchPredictionValidationAssessmentResultDict",
+    "BatchPredictionValidationAssessmentResultOrDict",
     "TuningResourceUsageAssessmentResult",
     "TuningResourceUsageAssessmentResultDict",
     "TuningResourceUsageAssessmentResultOrDict",

vertexai/_genai/types/common.py

@@ -14250,6 +14250,27 @@ class BatchPredictionResourceUsageAssessmentResultDict(TypedDict, total=False):
 ]
 
 
+class BatchPredictionValidationAssessmentResult(_common.BaseModel):
+    """Result of batch prediction validation assessment."""
+
+    errors: Optional[list[str]] = Field(
+        default=None, description="""The list of errors found in the dataset."""
+    )
+
+
+class BatchPredictionValidationAssessmentResultDict(TypedDict, total=False):
+    """Result of batch prediction validation assessment."""
+
+    errors: Optional[list[str]]
+    """The list of errors found in the dataset."""
+
+
+BatchPredictionValidationAssessmentResultOrDict = Union[
+    BatchPredictionValidationAssessmentResult,
+    BatchPredictionValidationAssessmentResultDict,
+]
+
+
 class TuningResourceUsageAssessmentResult(_common.BaseModel):
     """Result of tuning resource usage assessment."""